The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface
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Cites
Regression shrinkage and selection via the lasso
Tibshirani, R. (1996) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1996.tb02080.x · Cited by 51,482
Book Review: Pattern Recognition and Machine Learning
Nasrabadi, N. M. (2007) · Journal of Electronic Imaging · doi:10.1117/1.2819119 · Cited by 22,066
Regularization Paths for Generalized Linear Models via Coordinate Descent
Friedman, J., Hastie, T., & Tibshirani, R. (2010) · Journal of Statistical Software · doi:10.18637/jss.v033.i01 · Cited by 19,927
Brain–computer interfaces for communication and control
Wolpaw, J. R., Birbaumer, N., McFarland, D. J., Pfurtscheller, G., & Vaughan, T. M. (2002) · Clinical Neurophysiology · doi:10.1016/s1388-2457(02)00057-3 · Cited by 7,927
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.3.415 · Cited by 4,388
Pattern recognition and machine learning
(2007) · Choice Reviews Online · doi:10.5860/choice.44-5091 · Cited by 2,650
Optimal spatial filtering of single trial EEG during imagined hand movement
Ramoser, H., Muller-Gerking, J., & Pfurtscheller, G. (2000) · IEEE Transactions on Rehabilitation Engineering · doi:10.1109/86.895946 · Cited by 2,457
Optimizing Spatial filters for Robust EEG Single-Trial Analysis
Blankertz, B., Tomioka, R., Lemm, S., Kawanabe, M., & Muller, K.-R. (2008) · IEEE Signal Processing Magazine · doi:10.1109/msp.2008.4408441 · Cited by 2,080 · cited 2×
“Sparse Filter Band CSP The successful application of CSP to MI classification largely depends on the frequency band selection for bandpass filtering of EEG [23].”
BackgroundMethodMotor imagery and direct brain-computer communication
Pfurtscheller, G., & Neuper, C. (2001) · Proceedings of the IEEE · doi:10.1109/5.939829 · Cited by 1,990
Mu rhythm (de)synchronization and EEG single-trial classification of different motor imagery tasks
Pfurtscheller, G., Brunner, C., Schlögl, A., & Lopes da Silva, F. H. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2005.12.003 · Cited by 1,587
Cited by
A review of classification algorithms for EEG-based brain–computer interfaces: A 10 year update
Lotte, F., Bougrain, L., Cichocki, A., Clerc, M., Congedo, M., Rakotomamonjy, A., & Yger, F. (2018) · Journal of Neural Engineering · doi:10.1088/1741-2552/aab2f2 · Cited by 2,239
Self-citationInternal Feature Selection Method of CSP Based on L1-Norm and Dempster–Shafer Theory
Jin, J., Xiao, R., Daly, I., Miao, Y., Wang, X., & Cichocki, A. (2021) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015505 · Cited by 267
Self-citationSparse Bayesian Classification of EEG for Brain–Computer Interface
Zhang, Y., Zhou, G., Jin, J., Zhao, Q., Wang, X., & Cichocki, A. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2476656 · Cited by 264
Self-citation
Metadata
- Authors
- Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A.
- Venue
- Journal of Neuroscience Methods
- Publisher
- Elsevier BV
- Published
- 2015-11
- Volume
- 255
- Pages
- 85-91
- ISSN
- 0165-0270 (ISSN-L)
- OpenAlex
- W1147354400
- Cited by
- 266
- References
- 53
- FWCI
- 13.46
- Type
- journal article
- Language
- en
- Open access
- closed
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- Zhang, Y., Zhou, G., Jin, J., Wang, X., & Cichocki, A. (2015). Optimizing spatial patterns with sparse filter bands for motor-imagery based brain–computer interface. Journal of Neuroscience Methods. https://doi.org/10.1016/j.jneumeth.2015.08.004
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
